
S-LSTM [1]}. Dedicates LSTM for every pedestrian, and pool their states before predicting future steps. Their method only combines features of pedestrians who are found occupants of common neighborhood space. The neighborhood and occupancy grid sizes are set empirically for attaining the best results over ETH and UCY datasets.

MX-LSTM. A multi-cued model that encodes Vislets and Tracklets to estimate pedestrian trajectory using LSTM. It determines pedestrians relationships using fixed visual frustum that indicates their visual attention state.

SR-LSTM [2]}. A LSTM-based network inspired by message-passing graph neural networks [3]} which improves pedestrians motion representation and social neighborhoods for predicting future trajectory over fully-connected spatio-temporal graphs.

G-LSTM. Single Grid-LSTM cell that encodes pedestrian walking trajectories without additional cues. It doesn't include Sections REF  and REF  in its pipeline.

\(MC\)  Multi-Cue inference.
This model combines Vislets and Tracklets to predict pedestrians trajectories. It uses Vislets to incorporate pedestrian situational awareness into their motion modeling, however, it does not differentiate pedestrians influence.

\(MCR_n\)  Multi-Cue Relational inference.
Built upon the \(MC\)  model, it capitalizes on its understanding of situational awareness to differentiate pedestrians importance on each other for more accurate modeling of social interactions.

\(MCR_{mp}\)  Multi-Cue Relational inference. Uses message passing mechanism through virtual static neighborhood grid that directly passes importance values which reflects pedestrians social interaction.

\(MCR_{mpc}\) . In addition to the previous design components, this model encodes the visual static feature of the scene using a 2D convolutional layer to account for the contextual interaction between pedestrian and scene structure.

